목적
및
1조
소개
목적: 에임스(Ames) 도시의 허위매물을 피하자
1조: 송성필, 홍주형, 편서영, 양현준
Ames
도시
간략
설명
위치: [위키에서 긁어온 위치]
인구수: [위키에서 긁어온 인구수]
기타 주요 사항: [위키에서 추가적인 내용 작성]
Neighborhood
구역
구분
및
도식화
- high, mid, low로 구역 구분
- 해당 지역 이름을 보여주고, 군집 특성을 도식화
- 군집화된 지역들을 비슷한 특성대로 그룹화할 예정
Ames
도시
내
고유
지역
이름
(Neighborhood)
- Ames 도시의 29개 Neighborhood에 대한 설명
- 각 지역의 특성 및 인구수 등
사이드바
- 페이지의 다른 필터링이나 추가적인 옵션을 제공할 수 있는 공간
GDP and Life Expectancy
Population
Life Expectancy
''''RidgeCV(alphas=array([1.00000000e-04, 3.59381366e-04, 1.29154967e-03, 4.64158883e-03,
1.66810054e-02, 5.99484250e-02, 2.15443469e-01, 7.74263683e-01,
2.78255940e+00, 1.00000000e+01]),
cv=5, scoring='neg_mean_squared_error')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
RidgeCV(alphas=array([1.00000000e-04, 3.59381366e-04, 1.29154967e-03, 4.64158883e-03,
1.66810054e-02, 5.99484250e-02, 2.15443469e-01, 7.74263683e-01,
2.78255940e+00, 1.00000000e+01]),
cv=5, scoring='neg_mean_squared_error')RidgeCV(alphas=array([1.00000000e-04, 3.59381366e-04, 1.29154967e-03, 4.64158883e-03,
1.66810054e-02, 5.99484250e-02, 2.15443469e-01, 7.74263683e-01,
2.78255940e+00, 1.00000000e+01]),
cv=5, scoring='neg_mean_squared_error')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
RidgeCV(alphas=array([1.00000000e-04, 3.59381366e-04, 1.29154967e-03, 4.64158883e-03,
1.66810054e-02, 5.99484250e-02, 2.15443469e-01, 7.74263683e-01,
2.78255940e+00, 1.00000000e+01]),
cv=5, scoring='neg_mean_squared_error')RidgeCV(alphas=array([1.00000000e-04, 3.59381366e-04, 1.29154967e-03, 4.64158883e-03,
1.66810054e-02, 5.99484250e-02, 2.15443469e-01, 7.74263683e-01,
2.78255940e+00, 1.00000000e+01]),
cv=5, scoring='neg_mean_squared_error')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
RidgeCV(alphas=array([1.00000000e-04, 3.59381366e-04, 1.29154967e-03, 4.64158883e-03,
1.66810054e-02, 5.99484250e-02, 2.15443469e-01, 7.74263683e-01,
2.78255940e+00, 1.00000000e+01]),
cv=5, scoring='neg_mean_squared_error')RidgeCV(alphas=array([1.00000000e-04, 3.59381366e-04, 1.29154967e-03, 4.64158883e-03,
1.66810054e-02, 5.99484250e-02, 2.15443469e-01, 7.74263683e-01,
2.78255940e+00, 1.00000000e+01]),
cv=5, scoring='neg_mean_squared_error')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
RidgeCV(alphas=array([1.00000000e-04, 3.59381366e-04, 1.29154967e-03, 4.64158883e-03,
1.66810054e-02, 5.99484250e-02, 2.15443469e-01, 7.74263683e-01,
2.78255940e+00, 1.00000000e+01]),
cv=5, scoring='neg_mean_squared_error')RidgeCV(alphas=array([1.00000000e-04, 3.59381366e-04, 1.29154967e-03, 4.64158883e-03,
1.66810054e-02, 5.99484250e-02, 2.15443469e-01, 7.74263683e-01,
2.78255940e+00, 1.00000000e+01]),
cv=5, scoring='neg_mean_squared_error')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
RidgeCV(alphas=array([1.00000000e-04, 3.59381366e-04, 1.29154967e-03, 4.64158883e-03,
1.66810054e-02, 5.99484250e-02, 2.15443469e-01, 7.74263683e-01,
2.78255940e+00, 1.00000000e+01]),
cv=5, scoring='neg_mean_squared_error')RidgeCV(alphas=array([1.00000000e-04, 3.59381366e-04, 1.29154967e-03, 4.64158883e-03,
1.66810054e-02, 5.99484250e-02, 2.15443469e-01, 7.74263683e-01,
2.78255940e+00, 1.00000000e+01]),
cv=5, scoring='neg_mean_squared_error')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
RidgeCV(alphas=array([1.00000000e-04, 3.59381366e-04, 1.29154967e-03, 4.64158883e-03,
1.66810054e-02, 5.99484250e-02, 2.15443469e-01, 7.74263683e-01,
2.78255940e+00, 1.00000000e+01]),
cv=5, scoring='neg_mean_squared_error')점수제 허위매물
72개
회귀분석 허위매물
73개
최종 선정 허위매물
8개
| Neighborhood | PID | SalePrice | score | OverallQual | OverallCond | GrLivArea | YearRemodAdd | RoomDensity | amenities | |
|---|---|---|---|---|---|---|---|---|---|---|
| 374 | BrkSide | 903225160 | 106900 | 3 | 6 | 9 | 1290 | 2000 | 0.006202 | 3 |
| 2477 | Edwards | 909101010 | 110000 | 4 | 6 | 8 | 1196 | 2000 | 0.006689 | 3 |
| 585 | Mitchel | 923400040 | 160000 | 3 | 6 | 7 | 1750 | 1985 | 0.005143 | 3 |
| 997 | NAmes | 534427010 | 84900 | 4 | 5 | 6 | 1728 | 2001 | 0.006944 | 1 |
| 1225 | OldTown | 903430090 | 117000 | 3 | 6 | 8 | 1635 | 2003 | 0.003670 | 2 |
| 1909 | OldTown | 903476090 | 97500 | 3 | 7 | 5 | 1864 | 2000 | 0.006438 | 1 |
| 607 | SawyerW | 906226060 | 131000 | 3 | 5 | 7 | 2016 | 2007 | 0.003968 | 1 |
| 1008 | Timber | 916403200 | 204000 | 4 | 6 | 8 | 2237 | 2006 | 0.004470 | 3 |
📍 서로 다른 탐지 관점을 가지고 있기 때문에 두가지 방법의 결과가 상이하다고 판단
🏠 최종 결론
점수제를 통한 허위매물 탐지는 직관적인 기준에 기반해 빠르게 의심 매물을 걸러낼 수 있으며,
회귀 분석을 통한 방법은 패턴분석을 통해 정교한 판단을 할 수 있습니다.
두 가지 방법을 보완적으로 함께 활용할 경우,
단일 방법보다 더 높은 신뢰도로 허위매물 가능성이 높은 대상을 선별할 수 있다고 판단됩니다.